Unified real time rule analytics using common programming model on both edge and cloud

ABSTRACT

The system, method, and computer program product described herein provide unified real-time rule analytics to users through the use of an analytics logic editor that allows a user to construct an analytic logic rule unit that may be used on both edge and cloud devices. The user may select a data source, transform, rule condition, and action using an interface of the analytics logic editor to construct an analytics logic rule unit that may be deployed to both edge and cloud devices in real-time without the need to separately program each device. The analytics logic rule unit may be installed and executed by the edge and cloud device in real-time.

STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR

The following disclosure(s) are submitted under 35 U.S.C. 102(b)(1)(A):DISCLOSURE(S): (1) “IBM Edge Analytics Agent (EAA)” (part of the IBMWatson IoT Real Time Insight (RTI) Edge Component)”; Aug. 11, 2016 athttps://console.ng.bluemix.net/docs/services/IoT/edge_analytics.html.

BACKGROUND

The present disclosure relates to analytics on edge and cloud devices.

BRIEF SUMMARY

The system, method, and computer program product described hereinprovide unified real-time rule analytics to users through the use of ananalytics logic editor that allows a user to construct an analytic logicrule unit that may be used on both edge and cloud devices.

In an aspect of the present disclosure, a method is disclosed. Themethod includes receiving in real-time by an analytics logic editor ofan analytics system a plurality of user inputs from a computing deviceassociated with a user. The analytics logic editor is configured toconstruct an analytics logic rule in response to the received inputs.The analytics logic rule specifies a data source, a transform, a rulecondition, and an action. The plurality of user inputs includes anactivation of a first element of an interface associated with theanalytics logic editor. The activation of the first element selects thedata source for the analytics logic rule. The plurality of user inputsfurther includes an activation of a second element of the interface. Theactivation of the second element selects a transform to be applied todata received from the selected data source. The plurality of userinputs further includes an activation of a third element of theinterface. The activation of the third element selects a rule conditionto be applied to data that is transformed by the selected transform. Theplurality of user inputs further includes an activation of a fourthelement of the interface. The activation of the fourth element selectsan action to be taken in response to the data that is transformed by theselected transform meeting the selected rule condition. The methodfurther includes constructing the analytics logic rule based on theselected data source, selected transform, selected rule condition, andselected action, and transmitting the constructed analytics logic ruleto an edge device. The analytics logic rule is configured for real-timeinstallation and execution by the edge device upon receipt by the edgedevice.

In aspects of the present disclosure, apparatus, systems, and computerprogram products in accordance with the above aspect may also beprovided. Any of the above aspects may be combined without departingfrom the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of the present disclosure, both as to its structure andoperation, can best be understood by referring to the accompanyingdrawings, in which like reference numbers and designations refer to likeelements.

FIG. 1 is a system diagram illustrating a system in accordance with anaspect of the present disclosure.

FIG. 2 is a diagram illustrating an analytics logic rule unit accordingto an aspect of the present disclosure.

FIG. 3 is an illustration of an interface of the analytics logic editorof the system of FIG. 1 according to an aspect of the presentdisclosure.

FIG. 4 is an illustration of an interface of the analytics logic editorof the system of FIG. 1 according to a further aspect of the presentdisclosure.

FIG. 5 is an illustration of an interface of the analytics logic editorof the system of FIG. 1 according to a further aspect of the presentdisclosure.

FIG. 6 is an illustration of an interface of the analytics logic editorof the system of FIG. 1 according to a further aspect of the presentdisclosure.

FIG. 7 is an illustration of an interface of the analytics logic editorof the system of FIG. 1 according to a further aspect of the presentdisclosure.

FIG. 8 is a diagram illustrating the combination of two analytics logicrule units into one analytics logic rule unit according to an aspect ofthe present disclosure.

FIG. 9 is a diagram illustrating the use of the selected actions of twoanalytics logic rule units installed on the edge as selected datasources for an analytics logic rule unit installed on the cloudaccording to an aspect of the present disclosure.

FIG. 10 is a flow chart of a method according to an aspect of thepresent disclosure.

FIG. 11 is a flow chart of another method according to an aspect of thepresent disclosure.

FIG. 12 is an exemplary block diagram of a computer system in whichprocesses involved in the system, method, and computer program productdescribed herein may be implemented.

FIG. 13 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 14 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The present disclosure relates to the analysis of data generated at theedge, e.g., decentralized devices or sensors connected to a network. Insome aspects, data generated or gathered at the edge may be transmittedas raw data to a remote location such as a cloud server for analysis andprocessing by the cloud server. For example, an edge device such as atemperature sensor may transmit raw temperature data to the cloud serverfor processing according to a set of rules to determine an action to betaken.

As the technology utilized at the edge improves, processing and otherfunctions that were previously performed exclusively by cloud serversmay be offloaded onto the sensors or edge devices themselves or todevices near the edge devices or sensors, for example, onto an analyticsgateway positioned between the edge devices or sensors and the cloudserver.

One benefit of performing analytics at or near the edge is that thebandwidth usage between the edge and the cloud may be reduced. Forexample, where the edge generates large amounts of raw data,transmitting all of the raw data to the cloud may be costly in terms ofbandwidth usage. Processing or mining the raw data at or near the edgeallows the system to identify targeted and useful information from theraw data for subsequent transmission to the cloud without transmittingthe entire raw data. By extracting the useful data first at or near theedge, the size of the data to be transmitted to the cloud may be reducedthereby reducing the required bandwidth.

Processing at or near the edge may also reduce the storage requirementsat the cloud since cloud servers no longer need to store large volumesof raw data for further processing. Similarly, processing the raw dataas it is generated at the edge may also reduce the storage requirementsat the edge device, sensor, or analytics gateway since any data that isnot targeted or useful, e.g., not relevant, may be discarded at or nearthe point of generation through processing.

Often each edge device, sensor, or analytics gateway may requireseparate and distinct programming to handle the processing of raw data.For example, a programmer may be required to write code specifically foran edge device to filter or otherwise process the raw data and identifytargeted or useful information from the raw data for later transmissionto the cloud. Given the large number of potential edge devices, sensors,or analytics gateways that may be utilized to provide information to thecloud for use in analytics processing, the individual programming ofeach edge device, sensor, or analytics gateway may be inefficient.

The present disclosure provides systems and methods to allow a user togenerate analytics logic rules in real-time in an easy to define mannerfor real-time distribution to both edge and cloud devices.

With reference now to FIG. 1, a system 100 for implementing real-timerule analytics on both the edge 110 and cloud 150 is disclosed.

Edge 110 may include an edge gateway 112 and edge devices 130. Edgegateway 112 may include any device that is configured to performprocessing on data generated by edge devices 130. For example, edgegateway 112 may be a local computing device, server, or other similardevice that includes the capability to process or filter data generatedby edge devices 130 for later transmission to cloud 150. Althoughdescribed with reference to a single edge gateway 112, the use of anynumber of edge gateways 112 is contemplated without departing from thescope of the present disclosure.

Edge devices 130 may include any device that is capable of generatingdata. For example, edge devices 130 may include sensors, monitoringdevices, video capture devices, audio capture devices, or any otherdevice that may be used to generate data. In some aspects, edge devices130 may sometimes be called “things” in the context of the “internet ofthings” (IoT), e.g., the interconnection via the internet of computingdevices embedded in everyday objects to enable the objects to transmitand receive data. In some aspects, edge gateway 112 may be integratedwith or formed as a part of an edge device 130. For example, a smartsensor may be an edge device that is capable of both generating data andperforming processing or filtering on the generated data fortransmission to the cloud 150.

Edge gateway 112 may include a historian database 114, an analyticsengine 116, a gateway controller 118, a local message broker 120, and adevice adaptor 122.

Historian database 114 may store analytics data related to the edgegateway 112. For example, historian database 114 may store analyticslogic rules that are currently in use by the edge gateway 112, analyticslogic rules that were previously in use by the edge gateway 112,analytics logic rules that are currently inactive, or any otheranalytics related data for the edge gateway 112. In some aspectshistorian database 114 may store a current status of each analytic logicrule that has been installed on the edge gateway 112.

Analytics engine 116 is configured to execute the analytics logic rulesthat are stored in historian database 114. For example, with referenceto FIG. 2, each analytics engine 116 may embody or invoke an atomicanalytics logic rule 202 that specifies or defines a data source 204, atransform 206, a rule condition 208, and an action 210 to be applied toreceived data.

With reference now to FIGS. 1-3, data source 204 may include any edgedevice 130 that generates data or in some aspects, as an edge gateway112 that has data available for further processing. For example, datasource 204 may be defined as an edge device 130 such as a temperaturesensor. In some aspects, for example, where an edge device 130 maygenerate more than one kind of data, the data source 204 of the atomicanalytics rule logic 202 may define the edge device 130 and the specifictype of data that is to be used for the analytics analysis. In someaspects, data source 204 may define multiple data sources, e.g., morethan one edge device 130. In some aspects, for example, each data source204 or the data generated by the data sources 204 to be used by aparticular atomic analytics rule logic unit 202 may be required to be ofthe same type, e.g., all temperature data sources or temperature data.With reference to FIG. 3, for example, a data source 204 may beidentified by a device ID 302, e.g., sysSensor, mysensor2, gateway1_xw,etc., a type of device 304, e.g., engine sensors, gateway, etc., and aclass ID 306, e.g., device, gateway, etc.

Referring again to FIGS. 1-3, transform 206 may include any logic forconverting raw data generated by the data source 204 into a form usableby the atomic analytics rule logic 202. For example, each edge device130 may generate data in a different format depending on factors such ashow the edge device 130 is configured, programmed, the particularcomponents used by the edge device 130, or any other factors that mayaffect the format of the generated data. Transform 206 is configured toconvert the raw data from the format provided by the data source 204into a format that is usable by the edge gateway 112 to apply ruleconditions 208 to the data. As an example, if the data source generatestemperature data in Celsius but the rule condition 208 is defined withregard to temperatures in Fahrenheit, transform 206 may convert the rawdata from Celsius to Fahrenheit for use by rule condition 208. In someaspects, transform 206 may also or alternatively define a time dimensionfor applying the rule condition 208 to the raw data. For example,transform 206 may be defined to only provide transformed data to therule condition periodically instead of continuously, e.g., every 5minutes. In some aspects, transform 206 may average the raw data over aperiod of time, e.g., 5 minutes, and provide the average to the rulecondition 208. In some aspects, for example, with reference to FIG. 4,transform 206 may include a variety of processing options that may beapplied to transform the data including, for example, notation 402,averaging 404, smoothing 406, mathematical manipulation 408, forecasting410, or any other transformation.

Rule condition 208 may include rules logic for determining whether acertain condition has been met. With reference to FIG. 5, a rule mayinclude, for example, a data point, an operator, a comparison type(e.g., compare to a static value, compare to a data point, compare to acontext, etc.), and a value against which the data point is compared.With reference now to FIG. 6, an example rule condition 602 may includea data point of “oxygen”, a less than (“<”) operator, a compare withselection of a static value, and a value of “20.4”. Thus the examplerule condition 602 may be met when the oxygen value received from thedata source is less than 20.4 after any transformation, e.g., atransformation to the data received from the data source into anappropriate format that matches the format of the value, for example,converting the data source from degrees Fahrenheit to Celsius if theunit type of the value is also Celsius. In some aspects, FIG. 6 mayrepresent a user interface for constructing an atomic rule unit. Forexample, a user may activate an “or” +sign 606 to add additional atomiclogic rules units. As another example, the user may activate a “and”+sign 608 to add additional conditions for the current atomic logicrules unit, e.g., adding a new condition of temperature >35 to oxygen<20.4. As another example, the user may activate a +sign 610 to add anadditional action to be performed if the conditions are met.

Referring again to FIG. 2, action 210 defines an action to be performedwhen a rule condition is met. For example, if example rule condition 602(FIG. 6) is met, e.g., the oxygen received from the data source is lessthan 20.4 after any transformation, an example action may includenotifying a nurse, sounding an alarm, activating a medical device,transmitting a mail notification 604 (FIG. 6), sending raw data, datatransformed by transform 206, or rule-hit data, e.g., the output of anatomic analytics logic rule unit 202, to the cloud for furtherprocessing, or any other responsive action.

Referring again to FIG. 1, gateway controller 118 is configured tomanage the transmission and reception of messages or other data to andfrom the cloud. For example, gateway controller 118 may send any rulesreceived from the cloud to the analytics engine 116, or to deviceadaptors 122 via local message broker 120 and may send any raw data,transformed data, or outputs of analytics engine 16 to the cloud forfurther processing.

In an aspect, local message broker 120 may act as an interface betweenthe device adaptor 122 and the analytics engine 116 and gatewaycontroller 118. For example, local message broker 120 may handle thetransmission and reception of information, data, actions, or othermessages between device adaptor 122 on one side and one or both ofanalytics engine 116 and gateway controller 118.

Device adaptor 122 is configured for the transmission and reception ofdata and information between the edge gateway 112 and edge devices 130.In some aspects, device adaptor 122 may be configured to transmit andreceive messages in a variety of messaging protocols according to themessage protocols in used by each edge device 130. For example, deviceadaptor 122 may be configured to transmit and receive messages inprotocols including Message Queue Telemetry Transport (MQTT), HypertextTransfer Protocol (HTTP), Transmission Control Protocol (TCP)/UserDatagram Protocol (UDP), or other similar messaging protocols as needed.In some aspects, device adaptor 122 may include local MQTT Topics 124that may be used to categorize the various edge devices 130 that arecommunicating with the edge gateway 112.

Referring again to FIG. 1, the cloud 150 may include a messaging servicesystem 152 and a real-time analytics system 170.

Messaging service system 152 may include a messaging service 154,historian database 156, and a device manager 158.

Messaging service 154 is configured to transmit and receivecommunications, data, commands, analytics rules, or other informationbetween messaging service system 152 and real-time analytics system 170and also between messaging service system 152 and the edge 110, e.g.,edge gateway 112. Messaging service 154 is also configured tocommunicate with historian database 156 to store data received from edgegateway 112, analytics rules received from real-time analytics system170, or any other information, and to access any data stored onhistorian database 156.

Historian database 156 is similar to historian database 114 of edgegateway 112 and may store analytics data related to the edge gateway 112and real-time analytics system 170. For example, historian database 156may store analytics logic rules that are currently in use by the edgegateway 112, analytics logic rules that were previously in use by theedge gateway 112, analytics logic rules that are currently inactive, orany other analytics related data for the edge gateway 112. In someaspects historian database 114 may store a current status of eachanalytic logic rule that has been installed on the edge gateway 112.Historian database 156 may also store analytics logic rules that arecurrently in use by real-time analytics system 170, analytics logicrules that were previously in use by real-time analytics system 170,analytics logic rules that are currently inactive, or any otheranalytics related data for real-time analytics system 170. In someaspects historian database 156 may store a current status of eachanalytic logic rule that has been installed on real-time analyticssystem 170. In some aspects, historian database 156 may also store anydata received from edge gateway 112 including, for example, raw datagenerated by edge devices 130, preprocessed data output by analyticsengine 116 of edge gateway 112, or any other data on which analytics maybe performed by real-time analytics system 170. For example, messagingservice 154 may receive the data from edge gateway 112 and store thedata in historian database 156.

Device manager 158 is configured to manage the edge devices that areassociated with messaging service system 152. For example, any edgegateways 112 and edge devices 130 that are or have been in communicationwith messaging service system 152, for example via messaging service 154may be catalogued and managed by device manager 158. For example, devicemanager 158 may store a list of all edge gateways 112 and edge devices130 for use by real-time analytics system 170.

With continued reference to FIGS. 1 and 2, real-time analytics system170 includes an analytics engine 172 and an analytics logic editor 174.

Analytics engine 172 is configured to implement and execute analyticslogic rules in a similar manner as analytics engine 116 of edge gateway112. For example, analytics engine may execute atomic analytics logicrules 202 in a similar manner to analytics engine 116.

Analytics logic editor 174 includes an interface that is accessible byusers 176 to construct custom atomic analytics logic rules 202 forimplementation by analytics engines 116 and 172 without requiring usersto individually program device specific code for each edge device 130,edge gateway 112, or real-time analytics system 170. The analytics logiceditor 174 implements a single source unified programming model thatprovides a user with a way to control the processing of data on both theedge 110 and cloud 150 in an easy to use manner. In some aspects, forexample, rules constructed using analytics logic editor 174 may bepushed or propagated to both the edge 110 and cloud 150, e.g., to edgegateways 112, edge devices 130, messaging service system 152, andanalytics engine 172.

With reference again to FIG. 1-3, analytics logic editor 174 mayimplement an example interface 300 for selecting a data source 204 of acustom atomic analytics logic rule 202. As described above, interface300 includes an array of fields such as device ID 302, type of device304, and class ID 306. In some aspects, fields 302, 304, and 306 may bepopulated based on data received from device manager 158, for example,any connected edge gateways 112 and edge devices 130. In some aspects, auser of analytics logic editor 174, e.g., users 176, may activateelements 308 that correspond to each edge gateway 112, edge device 130,cloud based data source 204 or any other data source 204 to select thatdata source 204 as a data source 204 for use in a new atomic analyticslogic rule 202. For example, a user may activate an element 310 toselect the edge device having the device ID 302 of sysSensor as a datasource 204. In some aspects, the user 176 may activate more than oneelement 308 to select more than one data source 204 for use with asingle atomic analytics logic rule 202.

With reference again to FIGS. 1, 2 and 4, analytics logic editor 174 mayimplement an example interface 400 for selecting a transform 206 of acustom atomic analytics logic rule 202. Interface 400 identifies apayload name 412 and label 414, e.g., for the data source 204 selectedusing interface 300 (FIG. 3). A type of the payload (e.g., raw datareceived from the data source 204) to be received from the selected datasource 204 may be set using an element 416, e.g., string, integer, orother similar types. In some aspects, the type of payload may beautomatically set, for example, based on the type of the data source.Element 416 may, for example, be a drop down menu providing a selectionof data types.

In some aspects, the user may set payload properties 418 and notations402 to be applied to the payload. In some aspects, for example, apayload property may include a raw data property, e.g., a value, and atransformed property, e.g. an operator. The raw data property mayidentify the meaning of the data received from the device. The transformproperty may define the transform to be applied to the raw dataproperty.

In some aspects, for example, the raw data property may include a“value” that may be set by activation of a value element 420, e.g.,value element 420 may be activated to set a raw data property value of“2” or any other value.

In some aspects, the transform property may include one or moreoperations that may be set by the activation of one of operationelements 422. For example, the user may activate a combination of valueelements 420 and operation elements 422 as payload properties 418 todefine a formula to be applied to the payload. As an example, the usermay activate value elements 420 and operation elements 422 to define aformula of (payload+2)/3. As another example, the user may activatevalue elements 420 and operation elements 422 to define a formulaf=payload*9/5+32 to transform Celsius to Fahrenheit.

The selected payload properties 418 may be presented in a calculationsummary 424 for confirmation by the user 176.

Notations 402 may be used to further transform the result of the payloadproperties 418. For example, notations 402 may include elements 404,406, 408, and 410 that are activatable to apply transforms such as,e.g., averaging functions, smoothing functions, mathematical functions,forecasting functions, or any other transform. For example, the user maydefine a transform to compute “averaged” temperature every 5 minutes byactivating element 404 to apply an average function. As another example,the user can define a transform math.sin(payload) by activating element408 to apply a mathematical function. As another example, the user maydefine a transform to perform averaging on a time domain to computeaverage temperature by activating element 404 to apply an averagingfunction. As another example, the user may define a transform to smooththe time series curve to remove noise in the raw data by activatingelement 406 to apply smoothing. As another example, the user may definea transform to predict the data trend based on historical and currentincoming data by activating element 410 to apply forecasting.

With reference again to FIGS. 1, 2 and 5, analytics logic editor 174 mayimplement an example interface 500 for selecting a rule or condition ofa custom atomic analytics logic rule 202. In some aspects, interface 500includes a user selectable element 502 that is activatable by a user toconfigure the data type as a data point and a user selectable element504 that is activatable to configure the data type as a context. Forexample, the context may include at least one type of data schema suchas an asset schema, weather schema, or any other data type. In someaspects, for example, the context property may be pre-loaded. In someaspects, for example, an asset schema may include an asset ID, assettag, asset location, asset value, or other similar parameters. In someaspects, for example, when a user activates element 504 to select acontext, a list of schema and the corresponding properties may be listedfor further selection by the user. The rule may then be defined in partby selecting the corresponding property, for example, a rule may bedefined to perform an action if the asset ID=12345. It is contemplatedthat other elements may also be included that are activatable toconfigure the data type to other types of data without departing fromthe scope of the present disclosure.

In some aspects, where element 502 was activated for example, the usermay further activate a data point element 506 to select the target datapoint, e.g., the data source 204 or a payload property 418 (FIG. 4).

In some aspects, the user may activate one of operator elements 508 toselect an operator to be used with the selected data point as part ofthe condition logic. For example, the user may activate an operatorelement 508 corresponding to greater than (“>”), greater than or equalto (“>=”), less than (“<”), less than or equal to (“<=”), equal to(“==”), not equal to (“!=”), or any other operator that may be used tofor comparison of a data point.

In some aspects, the user may activate a compare with element 510 toselect a static value for comparison to the selected data point usingthe selected operator, or a compare with element 512 to select anotherdata point for comparison to the selected data point using the selectedoperator, or a compare with element 514 to select a context to compareto the selected data point using the selected operator. Any othercompare with element may be implemented for the selection of any otheritem of data for comparison to the selected data point using theselected operator.

In some aspects, the user may enter a value in a field 516 of interface500 for use in comparison with the selected data point using theselected operator. For example, in some aspects the field 516 may beactive for receiving a value when the compare with element 510 forselecting a static value has been selected. In some aspects, field 516may be replaced with another activatable element (not shown) forselection of a data point or context when one of compare with elements512 or 514 is activated.

In some aspects, the user may switch between interface 500 and interface400 by activating one of set condition element 518 and settransformation element 520.

With reference now to FIGS. 1, 2, and 7, analytics logic editor 174 mayimplement an example interface 700 for constructing an atomic analyticslogic rule 202. Interface 700 includes new condition element 702 that isactivatable by a user 176 to add a new condition to a rule, for example,via interface 500. For example, activation of new condition element 702may cause an analytics logic editor 174 to present interface 500 to theuser 176, for example, via a display of a computing device of the user176.

In some aspects, interface 700 may be configured to display a new actionelement 704 that is activatable by a user 176 to cause analytics logiceditor 174 to present an interface 706 for constructing an action of theatomic analytics logic rule 202. For example, interface 706 may includean add action element 708 that is activatable to create a new action.For example, the user may activate add action element 708 to add a newaction such as sending an alert to an e-mail address, sending devicedata to the cloud, notifying the user by an alarm, or any other action.The newly created action may then be selected for use by activation ofnew action element 704.

In some aspects, the user 176 may designate or flag a destination forthe constructed atomic analytics logic rule 202. For example, the usermay designate that the constructed atomic analytics logic rule 202 beused on the cloud 150, on the edge 110, or both. In some aspects, forexample, the user 176 may specify the particular edge gateways 112, edgedevices 130, analytics engine 172 or other targets for executing theconstructed atomic analytics logic rule 202. In some aspects, forexample, the user 176 may specify that the atomic analytics logic rule202 be provided for installation on any of analytics engine 172, edgegateways 112, and edge devices 130. For example, the user 176 may set aflag associated with the constructed atomic analytics logic rule 202 inanalytics logic editor 174 that specifies cloud only, edge only, or bothcloud and edge.

In some aspects, the user 176 may control or specify the distributionsof the atomic analytics logic rule 202 using analytics logic editor 174.For example, the user 176 may specify that the atomic analytics logicrule 202 be distributed to the relevant devices. In some aspects, forexample, a user 176 of analytics logic editor 174 may trigger theactivation or de-activation of the atomic analytics logic rule 202 afterit has already been distributed or deployed to the relevant devices. Forexample, upon initial deployment and installation at the relevantdevices, the atomic analytics logic rule 202 may be in an inactivestate. The user may then access analytics logic editor 174 to activatethe atomic analytics logic rule 202 for execution Likewise, the user mayalso access analytics logic editor 174 to deactivate the atomicanalytics logic rule 202 when the user no longer wishes for the atomicanalytics logic rule 202 to be actively executed by the relevant device,e.g., edge device 130, edge gateway 112, or analytics engine 172.

In some aspects, the analytics logic editor 174 may present the userwith a status of the atomic analytics logic rule 202 on the relevantdevice, e.g., edge device 130, edge gateway 112, or analytics engine172. For example, the status, e.g., distributed, installed, active,inactive, deactivated, or other similar statuses, may be presented tothe user 176 via analytics logic editor 174 and the user may be giventhe option to change the status, for example, as described above byactivating, deactivating, causing distribution, etc. For example, anelement (not shown) in analytics logic editor 174 may be activatable bya user to change the status.

With reference now to FIGS. 1, 2, and 8, in some aspects, atomicanalytics logic rules 202 may be merged together for form a singleatomic analytics logic rule 202. For example, analytics engine 116 oranalytics engine 172 may merge atomic analytics logic rules 202 togetherwhen the atomic analytics logic rules 202 have similar features, e.g.,data sources 204, rule conditions 208, or other features. For example,where two atomic analytics logic rules 202, e.g., atomic analytics logicrule 802 and atomic analytics logic rule 804 are installed on the samedevice, e.g., edge gateway 112, edge device 130, or analytics engine172, they may be combined to for a single atomic analytics logic rule806. In some aspects, this merging of atomic analytics logic rules 202may occur when the data source 204 is the same, e.g., both atomicanalytics logic rule 802 and atomic analytics logic rule 804 have thesame data source 204. For example, both atomic analytics logic rule 802and atomic analytics logic rule 804 may have a data source 204 of thesame oxygen sensor.

In some aspects, this merging of atomic analytics logic rules 202 mayoccur when the data source 204 is of the same type and have the samerule condition 208. For example, the merging may occur if both atomicanalytics logic rule 802 and atomic analytics logic rule 804 have a datasource 204 of the type, e.g., oxygen sensor, and the same rule, e.g.,oxygen<20.4.

With reference now to FIGS. 1, 2 and 9, in some aspects the actions 202output by multiple atomic analytics logic rules 202 from the edge 110,e.g., from edge gateways 112 or edge device 130, may be used as datasources 204 for the same atomic analytics logic rule 202 executing onthe cloud 150, e.g., at analytics engine 172. For example, an action 902of an atomic analytics logic rule 904 on a first edge gateway 112 oredge device 130 may be used as a data source 906 for an atomic analyticslogic rule 908 executing on cloud 150 Likewise, the action 910 of anatomic analytics logic rule 912 on a second edge gateway 112 or edgedevice 130 may also be used as a data source 906 for atomic analyticslogic rule 908 executing on cloud 150. For example, the atomic analyticslogic rule 908 may perform transformations 914, apply rule conditions916, and output actions 918 using both actions 902 and 910 as the datasource 906.

Referring now to FIG. 10, in an aspect of the present disclosure, anexample flowchart 1000 for the definition and distribution of analyticslogic is illustrated with further reference to FIGS. 1-6.

At 1002 a user, e.g., user 176 accesses analytics logic editor 174 toconstruct an atomic analytics logic rule 202 in real-time as describedabove.

At 1004, analytics logic editor 174 generates the atomic analytics logicrule 202 based on the activations and selections made by user 176, forexample, via interfaces 300, 400, 500, and 700.

At 1006, the atomic analytics logic rule 202 generated by the analyticslogic editor 174 is stored in the historian database 156 on the cloud150.

At 1008, the atomic analytics logic rule 202 generated by the analyticslogic editor 174 is deployed to the edge 110 and cloud 150 in real-time,e.g., to analytics engine 172, edge gateway 110, and edge device 130. Insome aspects, the user 176 may designate one or both of the edge 110 andcloud 150 for deployment.

At 1010, the atomic analytics logic rule 202 is provided to analyticsengine 172 on the cloud 150. Analytics engine 172 may install the atomicanalytics logic rule 202 and prepare for execution using the data source204 designated in the atomic analytics logic rule 202. The installationmay occur in real-time without requiring a restart of the analyticsengine 172.

At 1012, messaging service 154 transmits the atomic analytics logic rule202 to edge gateway controller 118 for deployment to edge gateway 110and edge devices 130.

At 1014, edge gateway controller 118 receives the atomic analytics logicrule 202 and sends the analytics logic rule 202 to analytics engine 116.

At 1016, analytics engine 116 installs the atomic analytics logic rule202 in real-time and prepares for execution of the atomic analyticslogic rule 202 using data from the designated data source 204 in theatomic analytics logic rule 202. In some aspects, the analytics engine116 may install and execute the atomic analytics logic rule 202 inreal-time without stopping the processing and execution of other logicrunes.

At 1018, the atomic analytics logic rule 202 may also be stored in thehistorian database 114 of the edge gateway 112.

Referring now to FIG. 11, in an aspect of the present disclosure, anexample flowchart 1100 of an example edge side implementation isillustrated with further reference to FIGS. 1-6.

At 1102, messaging service 154 transmits a device connection metadescriptor, e.g., a MQTT message, to the edge 110, e.g., edge gateway112 or edge device 130. The device connection meta descriptor includesdata identifying a target edge gateway 110 or edge device 130 forinstallation of a constructed atomic analytics logic rule 202. In someaspects, for example, the device connection meta descriptor describeshow gateway device 112 or edge device 130 can receive the raw devicedata and how to recognize the data that is received. When the deviceconnection meta descriptor is installed on the analytics engine 116, forexample, the analytics engine gains the capability to parse and the rawdata received from the associated device.

At 1104, cloud 150, e.g., messaging service 154, transmits a ruleconnection descriptor, e.g., a MQTT message, to the edge 110, e.g., edgegateway 112 or edge device 130. The rule connection descriptor includesan atomic analytics logic rule 202 constructed by a user 176 forimplementation on the edge 110.

At 1106, the edge 110, e.g., edge gateway 112 or edge device 130,receives the device connection meta descriptor and rule connectiondescriptor from cloud 150, e.g., from messaging service 154.

At 1108, the historian database 114 on the edge stores the data from thereceived device connection meta descriptor and the atomic analyticslogic rule 202 from the received rule connection descriptor.

At 1110, the edge gateway 112 or edge device 130 translates and updatesthe received atomic analytics logic rule 202 for installation andexecution by the edge gateway 112 or edge device 130. For example,analytics engine 116 may receive meta descriptor from 1102 and gain thecapability to translate or parse any data received from the target edgedevice at 1112 and may receive the rule connection descriptor from 1104which defines the rule to be applied to the data received from thetarget edge device at 1112.

At 1112, a raw data is generated and provided or transmitted to edgegateway 112 or edge device 130, in a data message connection descriptor,e.g., a MQTT message. For example, the raw data may be generated by asensor on the edge 110, e.g., by a sensor associated with an edgegateway 112, by an edge device 130 or by any other data source 204.

At 1114, the data message connection descriptor is received by the edgegateway 112 or edge device 130. For example, in some aspects an edgegateway 112 may receive data message connection descriptor from an edgedevice 130 that generates raw data. In some aspects, an edge device 130that is configured to process data and execute atomic analytics logicrules 202 may both generate and receive the raw data at 1112 and 1114.

At 1116, the edge gateway 112 or edge device 130 sends heartbeatmessages that monitor the health of the atomic analytics logic rule 202to analytics engine 116. For example, the heartbeat messages may includean indication of whether the rules are executing properly, if there areany error conditions, or any other health related status of the atomicanalytics logic rule 202 or analytics engine 116.

At 1118, analytics engine 116 executes the logic of the atomic analyticslogic rule 202 on the raw data from the data message connectiondescriptor.

At 1120, the outcome of the executed logic is transmitted to messagingservice 154 on the cloud 150 based on the actions set in the analyticslogic rule 202.

At 1122, the Mbean monitors the health and status metrics of theanalytics engine 116 to determine whether the atomic analytics logic 202has executed properly and whether analytics engine 116 has encounteredany failures or anomalies. For example, Mbean may monitor the executionsof atomic analytics logic rules 202 by analytics engine 116 to determinewhether a failure has occurred either in the atomic analytics logicrules 202 or the analysis engine 116 itself. In some aspects, forexample, the Mbean monitoring may track statistics including last heartbeat, byte in and byte out, and other similar characteristics of theexecution by analytics engine 116.

At 1124, the monitoring output, e.g., the status of atomic analyticslogic rules 202 and analysis engine 116, may be transmitted to an Mbeanserver for monitoring the flow of the analytics engine 116.

At 1126, when a failure has occurred, the failure may be handled bystreaming runtime flow failover handling by the Mbean server. Forexample, the MBean server may determine that a failure has occurredbased on the Mbean status messages and may handle the failure, forexample, by restarting the analytics engine 116 or the flow of data tothe analytics engine 116.

FIG. 12 illustrates a schematic of an example computer or processingsystem that may implement any portion of system 100, edge 100, edgegateway 112, edge devices 130, cloud 150, messaging service system 152,real-time analytics system 170, analytics engine 116, analytics engine172, analytics logic editor 174, computing devices associated with users176, systems, methods, and computer program products described herein inone embodiment of the present disclosure. The computer system is onlyone example of a suitable processing system and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the methodology described herein. The processing systemshown may be operational with numerous other general purpose or specialpurpose computing system environments or configurations. Examples ofwell-known computing systems, environments, and/or configurations thatmay be suitable for use with the processing system may include, but arenot limited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a software module 10 thatperforms the methods described herein. The module 10 may be programmedinto the integrated circuits of the processor 12, or loaded from memory16, storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 13, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 13 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 14, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 1) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 2 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and analytics logic editing 96.

Although specific embodiments of the present invention have beendescribed, it will be understood by those of skill in the art that thereare other embodiments that are equivalent to the described embodiments.Accordingly, it is to be understood that the invention is not to belimited by the specific illustrated embodiments, but only by the scopeof the appended claims.

What is claimed is:
 1. A method implemented by at least one hardwareprocessor comprising: receiving in real-time by an analytics logiceditor of an analytics system a plurality of user inputs from acomputing device associated with a user, the analytics logic editorconfigured to construct an analytics logic rule in response to thereceived inputs, the analytics logic rule specifying a data source, atransform, a rule condition, and an action, the plurality of user inputscomprising: an activation of a first element of an interface associatedwith the analytics logic editor, the activation of the first elementselecting the data source for the analytics logic rule; an activation ofa second element of the interface, the activation of the second elementselecting a transform to be applied to data received from the selecteddata source; an activation of a third element of the interface, theactivation of the third element selecting a rule condition to be appliedto data that is transformed by the selected transform; an activation ofa fourth element of the interface, the activation of the fourth elementselecting an action to be taken in response to the data that istransformed by the selected transform meeting the selected rulecondition; constructing the analytics logic rule based on the selecteddata source, selected transform, selected rule condition, and selectedaction; and transmitting the constructed analytics logic rule to an edgedevice, the analytics logic rule configured for real-time installationand execution by the edge device upon receipt by the edge device.
 2. Themethod of claim 1, wherein the plurality of user inputs furthercomprises: an activation of a fifth element of the interface, theactivation of the fifth element setting a deployment of the constructedanalytics logic rule to a destination consisting of an edge device, acloud device, or both.
 3. The method of claim 1, further comprisingconstructing a second analytics logic rule based on a second pluralityof user inputs, the second analytics logic rule comprising a selecteddata source, selected transform, selected rule condition, and selectedaction, the selected data source of the second analytics logic rulecomprising the selected action of the constructed analytics logic rule.4. The method of claim 3, further comprising: receiving data from theedge device, the data comprising an output of the selected action of theanalytics logic rule; and executing the second analytics logic rule byan analytics engine residing on a cloud device based on the receiveddata from the edge device.
 5. The method of claim 3, wherein theselected data source of the second analytics logic rule comprises theselection action of the constructed analytics logic rule and a selectedaction of at least a constructed third analytics logic rule.
 6. Themethod of claim 1, further comprising: constructing a second analyticslogic rule based on a second plurality of user inputs, the secondanalytics logic rule comprising a selected data source, selectedtransform, selected rule condition, and selected action; andtransmitting the second analytics logic rule to the edge device, thesecond analytics logic rule configured for real-time installation andexecution by the edge device upon receipt by the edge device, whereinthe second analytics logic rule is configured for combination with theanalytics logic rule by the edge device to form a third analytics logicrule, the third analytics logic rule comprising: a data sourcecomprising the selected data source of the analytics logic rule and theselected data source of the second analytics logic rule; a transformcomprising the selected transform of the analytics logic rule and theselected transform of the second analytics logic rule; a rule conditioncomprising the selected rule condition of the analytics logic rule andthe selected rule condition of the second analytics logic rule; and anaction comprising the selected action of the analytics logic rule andthe selected action of the second analytics logic rule.
 7. The method ofclaim 1, wherein the analytics logic rule is configured for real-timeinstallation and execution on both the edge device and a cloud device.